Overview

Dataset statistics

Number of variables19
Number of observations12441
Missing cells0
Missing cells (%)0.0%
Duplicate rows93
Duplicate rows (%)0.7%
Total size in memory2.2 MiB
Average record size in memory181.2 B

Variable types

Numeric16
Categorical3

Alerts

Dataset has 93 (0.7%) duplicate rowsDuplicates
TotalSteps is highly overall correlated with TotalDistance and 6 other fieldsHigh correlation
TotalDistance is highly overall correlated with TotalSteps and 7 other fieldsHigh correlation
TrackerDistance is highly overall correlated with TotalSteps and 7 other fieldsHigh correlation
VeryActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightActiveDistance is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly overall correlated with TotalSteps and 6 other fieldsHigh correlation
FairlyActiveMinutes is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightlyActiveMinutes is highly overall correlated with LightActiveDistanceHigh correlation
Calories is highly overall correlated with TotalDistance and 2 other fieldsHigh correlation
TotalMinutesAsleep is highly overall correlated with TotalTimeInBedHigh correlation
TotalTimeInBed is highly overall correlated with TotalMinutesAsleepHigh correlation
TotalSleepRecords is highly imbalanced (65.3%)Imbalance
TotalSteps has 612 (4.9%) zerosZeros
TotalDistance has 615 (4.9%) zerosZeros
TrackerDistance has 615 (4.9%) zerosZeros
LoggedActivitiesDistance has 11809 (94.9%) zerosZeros
VeryActiveDistance has 4675 (37.6%) zerosZeros
ModeratelyActiveDistance has 4350 (35.0%) zerosZeros
LightActiveDistance has 665 (5.3%) zerosZeros
SedentaryActiveDistance has 12305 (98.9%) zerosZeros
VeryActiveMinutes has 4675 (37.6%) zerosZeros
FairlyActiveMinutes has 4335 (34.8%) zerosZeros
LightlyActiveMinutes has 662 (5.3%) zerosZeros

Reproduction

Analysis started2023-01-21 19:58:25.902941
Analysis finished2023-01-21 19:58:44.605486
Duration18.7 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0270125 × 109
Minimum1.5039604 × 109
Maximum8.7920097 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:44.655982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5039604 × 109
5-th percentile1.5039604 × 109
Q13.9773337 × 109
median4.7029217 × 109
Q36.9621811 × 109
95-th percentile8.3785632 × 109
Maximum8.7920097 × 109
Range7.2880493 × 109
Interquartile range (IQR)2.9848474 × 109

Descriptive statistics

Standard deviation2.0478087 × 109
Coefficient of variation (CV)0.40736097
Kurtosis-0.73125409
Mean5.0270125 × 109
Median Absolute Deviation (MAD)8.7422863 × 108
Skewness0.009556549
Sum6.2541063 × 1013
Variance4.1935205 × 1018
MonotonicityIncreasing
2023-01-21T11:58:44.716839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
8378563200 992
 
8.0%
6962181067 961
 
7.7%
5553957443 961
 
7.7%
4702921684 868
 
7.0%
2026352035 868
 
7.0%
4445114986 868
 
7.0%
3977333714 840
 
6.8%
4319703577 806
 
6.5%
5577150313 780
 
6.3%
1503960366 775
 
6.2%
Other values (14) 3722
29.9%
ValueCountFrequency (%)
1503960366 775
6.2%
1644430081 120
 
1.0%
1844505072 93
 
0.7%
1927972279 155
 
1.2%
2026352035 868
7.0%
2320127002 31
 
0.2%
2347167796 270
 
2.2%
3977333714 840
6.8%
4020332650 248
 
2.0%
4319703577 806
6.5%
ValueCountFrequency (%)
8792009665 435
3.5%
8378563200 992
8.0%
8053475328 93
 
0.7%
7086361926 744
6.0%
7007744171 52
 
0.4%
6962181067 961
7.7%
6775888955 78
 
0.6%
6117666160 504
4.1%
5577150313 780
6.3%
5553957443 961
7.7%

ActivityDate
Categorical

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size194.4 KiB
4/12/2016
 
413
4/22/2016
 
413
4/29/2016
 
413
4/28/2016
 
413
4/13/2016
 
413
Other values (26)
10376 

Length

Max length9
Median length9
Mean length8.7128848
Min length8

Characters and Unicode

Total characters108397
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/12/2016
2nd row4/12/2016
3rd row4/12/2016
4th row4/12/2016
5th row4/12/2016

Common Values

ValueCountFrequency (%)
4/12/2016 413
 
3.3%
4/22/2016 413
 
3.3%
4/29/2016 413
 
3.3%
4/28/2016 413
 
3.3%
4/13/2016 413
 
3.3%
4/26/2016 413
 
3.3%
4/25/2016 413
 
3.3%
4/24/2016 413
 
3.3%
4/23/2016 413
 
3.3%
4/27/2016 413
 
3.3%
Other values (21) 8311
66.8%

Length

2023-01-21T11:58:44.778906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4/12/2016 413
 
3.3%
4/21/2016 413
 
3.3%
4/20/2016 413
 
3.3%
4/14/2016 413
 
3.3%
4/22/2016 413
 
3.3%
4/16/2016 413
 
3.3%
4/17/2016 413
 
3.3%
4/18/2016 413
 
3.3%
4/19/2016 413
 
3.3%
4/15/2016 413
 
3.3%
Other values (21) 8311
66.8%

Most occurring characters

ValueCountFrequency (%)
/ 24882
23.0%
2 18097
16.7%
1 17953
16.6%
6 13665
12.6%
0 13627
12.6%
4 9056
 
8.4%
5 5833
 
5.4%
3 1622
 
1.5%
7 1224
 
1.1%
9 1219
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83515
77.0%
Other Punctuation 24882
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 18097
21.7%
1 17953
21.5%
6 13665
16.4%
0 13627
16.3%
4 9056
10.8%
5 5833
 
7.0%
3 1622
 
1.9%
7 1224
 
1.5%
9 1219
 
1.5%
8 1219
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 24882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108397
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 24882
23.0%
2 18097
16.7%
1 17953
16.6%
6 13665
12.6%
0 13627
12.6%
4 9056
 
8.4%
5 5833
 
5.4%
3 1622
 
1.5%
7 1224
 
1.1%
9 1219
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 24882
23.0%
2 18097
16.7%
1 17953
16.6%
6 13665
12.6%
0 13627
12.6%
4 9056
 
8.4%
5 5833
 
5.4%
3 1622
 
1.5%
7 1224
 
1.1%
9 1219
 
1.1%

TotalSteps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct635
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8117.3092
Minimum0
Maximum22988
Zeros612
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:44.840959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q14660
median8596
Q311317
95-th percentile15050
Maximum22988
Range22988
Interquartile range (IQR)6657

Descriptive statistics

Standard deviation4478.6354
Coefficient of variation (CV)0.55173892
Kurtosis-0.43319282
Mean8117.3092
Median Absolute Deviation (MAD)3364
Skewness0.017725869
Sum1.0098744 × 108
Variance20058175
MonotonicityNot monotonic
2023-01-21T11:58:44.999982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 612
 
4.9%
9105 56
 
0.5%
12764 56
 
0.5%
5232 33
 
0.3%
13318 32
 
0.3%
11207 32
 
0.3%
2132 32
 
0.3%
13630 32
 
0.3%
13070 32
 
0.3%
6582 32
 
0.3%
Other values (625) 11492
92.4%
ValueCountFrequency (%)
0 612
4.9%
4 3
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
16 8
 
0.1%
17 26
 
0.2%
29 26
 
0.2%
31 24
 
0.2%
42 15
 
0.1%
44 3
 
< 0.1%
ValueCountFrequency (%)
22988 3
 
< 0.1%
22770 24
0.2%
22359 3
 
< 0.1%
22244 15
0.1%
22026 3
 
< 0.1%
20669 3
 
< 0.1%
20500 3
 
< 0.1%
20159 3
 
< 0.1%
20067 2
 
< 0.1%
20031 31
0.2%

TotalDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct499
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7347384
Minimum0
Maximum17.950001
Zeros615
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:45.072247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0099999998
Q13.1800001
median6.1199999
Q37.9200001
95-th percentile10.71
Maximum17.950001
Range17.950001
Interquartile range (IQR)4.74

Descriptive statistics

Standard deviation3.2435404
Coefficient of variation (CV)0.56559519
Kurtosis-0.23799548
Mean5.7347384
Median Absolute Deviation (MAD)2.3599997
Skewness0.11381159
Sum71345.88
Variance10.520555
MonotonicityNot monotonic
2023-01-21T11:58:45.143679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 615
 
4.9%
6.710000038 114
 
0.9%
6.820000172 84
 
0.7%
7.099999905 79
 
0.6%
2.559999943 78
 
0.6%
3.619999886 71
 
0.6%
2.599999905 70
 
0.6%
0.009999999776 64
 
0.5%
2.769999981 62
 
0.5%
7.630000114 62
 
0.5%
Other values (489) 11142
89.6%
ValueCountFrequency (%)
0 615
4.9%
0.009999999776 64
 
0.5%
0.01999999955 26
 
0.2%
0.02999999933 18
 
0.1%
0.03999999911 8
 
0.1%
0.07999999821 8
 
0.1%
0.09000000358 15
 
0.1%
0.1000000015 5
 
< 0.1%
0.1099999994 5
 
< 0.1%
0.1299999952 3
 
< 0.1%
ValueCountFrequency (%)
17.95000076 3
 
< 0.1%
17.64999962 3
 
< 0.1%
17.54000092 24
0.2%
17.19000053 3
 
< 0.1%
16.23999977 3
 
< 0.1%
15.97000027 3
 
< 0.1%
15.68999958 3
 
< 0.1%
15.67000008 3
 
< 0.1%
15.07999992 15
0.1%
15.01000023 18
0.1%

TrackerDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct497
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7277679
Minimum0
Maximum17.950001
Zeros615
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:45.220143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0099999998
Q13.1800001
median6.1199999
Q37.8899999
95-th percentile10.71
Maximum17.950001
Range17.950001
Interquartile range (IQR)4.7099998

Descriptive statistics

Standard deviation3.2359541
Coefficient of variation (CV)0.56495902
Kurtosis-0.22650491
Mean5.7277679
Median Absolute Deviation (MAD)2.3599997
Skewness0.11260323
Sum71259.16
Variance10.471399
MonotonicityNot monotonic
2023-01-21T11:58:45.292223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 615
 
4.9%
6.710000038 114
 
0.9%
6.820000172 84
 
0.7%
7.099999905 79
 
0.6%
2.559999943 78
 
0.6%
3.619999886 71
 
0.6%
2.599999905 70
 
0.6%
0.009999999776 64
 
0.5%
7.630000114 62
 
0.5%
9.079999924 62
 
0.5%
Other values (487) 11142
89.6%
ValueCountFrequency (%)
0 615
4.9%
0.009999999776 64
 
0.5%
0.01999999955 26
 
0.2%
0.02999999933 18
 
0.1%
0.03999999911 8
 
0.1%
0.07999999821 8
 
0.1%
0.09000000358 15
 
0.1%
0.1000000015 5
 
< 0.1%
0.1099999994 5
 
< 0.1%
0.1299999952 3
 
< 0.1%
ValueCountFrequency (%)
17.95000076 3
 
< 0.1%
17.64999962 3
 
< 0.1%
17.54000092 24
0.2%
17.19000053 3
 
< 0.1%
16.23999977 3
 
< 0.1%
15.97000027 3
 
< 0.1%
15.68999958 3
 
< 0.1%
15.67000008 3
 
< 0.1%
15.07999992 15
0.1%
15.01000023 18
0.1%

LoggedActivitiesDistance
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12333042
Minimum0
Maximum4.942142
Zeros11809
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:45.357194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.0921471
Maximum4.942142
Range4.942142
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55307096
Coefficient of variation (CV)4.4844653
Kurtosis24.072073
Mean0.12333042
Median Absolute Deviation (MAD)0
Skewness4.7613355
Sum1534.3537
Variance0.30588749
MonotonicityNot monotonic
2023-01-21T11:58:45.414110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 11809
94.9%
2.092147112 288
 
2.3%
2.253081083 224
 
1.8%
4.081692219 31
 
0.2%
3.167821884 31
 
0.2%
2.785175085 31
 
0.2%
1.959596038 3
 
< 0.1%
4.885604858 2
 
< 0.1%
4.878232002 2
 
< 0.1%
4.912367821 2
 
< 0.1%
Other values (9) 18
 
0.1%
ValueCountFrequency (%)
0 11809
94.9%
1.959596038 3
 
< 0.1%
2.092147112 288
 
2.3%
2.253081083 224
 
1.8%
2.785175085 31
 
0.2%
2.832325935 2
 
< 0.1%
3.167821884 31
 
0.2%
3.285414934 2
 
< 0.1%
4.081692219 31
 
0.2%
4.851306915 2
 
< 0.1%
ValueCountFrequency (%)
4.94214201 2
< 0.1%
4.930550098 2
< 0.1%
4.924840927 2
< 0.1%
4.912367821 2
< 0.1%
4.911146164 2
< 0.1%
4.885604858 2
< 0.1%
4.878232002 2
< 0.1%
4.869782925 2
< 0.1%
4.861792088 2
< 0.1%
4.851306915 2
< 0.1%

VeryActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct276
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3986135
Minimum0
Maximum13.4
Zeros4675
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:45.480444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.52999997
Q32.3099999
95-th percentile5.29
Maximum13.4
Range13.4
Interquartile range (IQR)2.3099999

Descriptive statistics

Standard deviation1.9127301
Coefficient of variation (CV)1.3675902
Kurtosis4.2411551
Mean1.3986135
Median Absolute Deviation (MAD)0.52999997
Skewness1.8465199
Sum17400.15
Variance3.6585364
MonotonicityNot monotonic
2023-01-21T11:58:45.557541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4675
37.6%
0.0700000003 134
 
1.1%
2.029999971 98
 
0.8%
1.370000005 88
 
0.7%
1.059999943 84
 
0.7%
0.3199999928 82
 
0.7%
0.3700000048 80
 
0.6%
0.05999999866 80
 
0.6%
2.789999962 79
 
0.6%
0.3300000131 78
 
0.6%
Other values (266) 6963
56.0%
ValueCountFrequency (%)
0 4675
37.6%
0.01999999955 20
 
0.2%
0.03999999911 15
 
0.1%
0.05000000075 20
 
0.2%
0.05999999866 80
 
0.6%
0.0700000003 134
 
1.1%
0.07999999821 71
 
0.6%
0.09000000358 28
 
0.2%
0.1099999994 34
 
0.3%
0.1199999973 31
 
0.2%
ValueCountFrequency (%)
13.39999962 3
 
< 0.1%
13.26000023 3
 
< 0.1%
13.13000011 3
 
< 0.1%
12.53999996 3
 
< 0.1%
12.43999958 3
 
< 0.1%
12.34000015 3
 
< 0.1%
11.64000034 3
 
< 0.1%
11.36999989 3
 
< 0.1%
10.43000031 3
 
< 0.1%
9.890000343 24
0.2%

ModeratelyActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73219034
Minimum0
Maximum6.48
Zeros4350
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:45.633396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.40000001
Q31
95-th percentile2.45
Maximum6.48
Range6.48
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0374765
Coefficient of variation (CV)1.4169492
Kurtosis7.8551834
Mean0.73219034
Median Absolute Deviation (MAD)0.40000001
Skewness2.5230584
Sum9109.18
Variance1.0763575
MonotonicityNot monotonic
2023-01-21T11:58:45.709826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4350
35.0%
0.400000006 171
 
1.4%
0.25 150
 
1.2%
0.7900000215 137
 
1.1%
0.4199999869 119
 
1.0%
0.1899999976 118
 
0.9%
0.5699999928 116
 
0.9%
0.6399999857 114
 
0.9%
0.2800000012 112
 
0.9%
0.9200000167 111
 
0.9%
Other values (182) 6943
55.8%
ValueCountFrequency (%)
0 4350
35.0%
0.02999999933 33
 
0.3%
0.03999999911 36
 
0.3%
0.05000000075 15
 
0.1%
0.05999999866 7
 
0.1%
0.07999999821 24
 
0.2%
0.1099999994 28
 
0.2%
0.1199999973 32
 
0.3%
0.1400000006 26
 
0.2%
0.150000006 57
 
0.5%
ValueCountFrequency (%)
6.480000019 28
0.2%
6.210000038 28
0.2%
5.599999905 28
0.2%
5.400000095 28
0.2%
5.239999771 28
0.2%
5.119999886 28
0.2%
4.579999924 28
0.2%
4.559999943 28
0.2%
4.349999905 28
0.2%
4.21999979 56
0.5%

LightActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct412
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5417708
Minimum0
Maximum10.3
Zeros665
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:45.788234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.3699999
median3.54
Q34.8299999
95-th percentile6.46
Maximum10.3
Range10.3
Interquartile range (IQR)2.46

Descriptive statistics

Standard deviation1.8785532
Coefficient of variation (CV)0.53039943
Kurtosis-0.039584791
Mean3.5417708
Median Absolute Deviation (MAD)1.23
Skewness0.11380999
Sum44063.17
Variance3.5289621
MonotonicityNot monotonic
2023-01-21T11:58:45.861534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 665
 
5.3%
4.179999828 124
 
1.0%
4.710000038 114
 
0.9%
2.470000029 110
 
0.9%
2.670000076 106
 
0.9%
3.910000086 103
 
0.8%
4.5 95
 
0.8%
3.920000076 92
 
0.7%
5.409999847 90
 
0.7%
3.769999981 90
 
0.7%
Other values (402) 10852
87.2%
ValueCountFrequency (%)
0 665
5.3%
0.009999999776 64
 
0.5%
0.01999999955 26
 
0.2%
0.02999999933 26
 
0.2%
0.03999999911 8
 
0.1%
0.05999999866 3
 
< 0.1%
0.09000000358 15
 
0.1%
0.1000000015 5
 
< 0.1%
0.1099999994 5
 
< 0.1%
0.1299999952 31
 
0.2%
ValueCountFrequency (%)
10.30000019 18
0.1%
9.479999542 18
0.1%
9.460000038 4
 
< 0.1%
8.970000267 28
0.2%
8.680000305 18
0.1%
8.409999847 18
0.1%
8.270000458 28
0.2%
8.260000229 2
 
< 0.1%
8.229999542 2
 
< 0.1%
7.949999809 2
 
< 0.1%

SedentaryActiveDistance
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00067438309
Minimum0
Maximum0.11
Zeros12305
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:45.921211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.11
Range0.11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0078133467
Coefficient of variation (CV)11.585917
Kurtosis163.13137
Mean0.00067438309
Median Absolute Deviation (MAD)0
Skewness12.657826
Sum8.39
Variance6.1048387 × 10-5
MonotonicityNot monotonic
2023-01-21T11:58:45.970114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 12305
98.9%
0.009999999776 40
 
0.3%
0.1099999994 31
 
0.2%
0.1000000015 31
 
0.2%
0.01999999955 10
 
0.1%
0.05000000075 8
 
0.1%
0.0700000003 8
 
0.1%
0.03999999911 8
 
0.1%
ValueCountFrequency (%)
0 12305
98.9%
0.009999999776 40
 
0.3%
0.01999999955 10
 
0.1%
0.03999999911 8
 
0.1%
0.05000000075 8
 
0.1%
0.0700000003 8
 
0.1%
0.1000000015 31
 
0.2%
0.1099999994 31
 
0.2%
ValueCountFrequency (%)
0.1099999994 31
 
0.2%
0.1000000015 31
 
0.2%
0.0700000003 8
 
0.1%
0.05000000075 8
 
0.1%
0.03999999911 8
 
0.1%
0.01999999955 10
 
0.1%
0.009999999776 40
 
0.3%
0 12305
98.9%

VeryActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.973555
Minimum0
Maximum210
Zeros4675
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:46.035845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q336
95-th percentile97
Maximum210
Range210
Interquartile range (IQR)36

Descriptive statistics

Standard deviation34.911291
Coefficient of variation (CV)1.4562417
Kurtosis6.2266079
Mean23.973555
Median Absolute Deviation (MAD)8
Skewness2.2098318
Sum298255
Variance1218.7983
MonotonicityNot monotonic
2023-01-21T11:58:46.105540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4675
37.6%
1 376
 
3.0%
8 302
 
2.4%
19 240
 
1.9%
3 236
 
1.9%
6 209
 
1.7%
14 205
 
1.6%
4 185
 
1.5%
11 172
 
1.4%
30 169
 
1.4%
Other values (104) 5672
45.6%
ValueCountFrequency (%)
0 4675
37.6%
1 376
 
3.0%
2 136
 
1.1%
3 236
 
1.9%
4 185
 
1.5%
5 156
 
1.3%
6 209
 
1.7%
7 111
 
0.9%
8 302
 
2.4%
9 106
 
0.9%
ValueCountFrequency (%)
210 26
0.2%
207 26
0.2%
200 26
0.2%
194 26
0.2%
184 26
0.2%
137 32
0.3%
132 3
 
< 0.1%
129 3
 
< 0.1%
125 6
 
< 0.1%
123 32
0.3%

FairlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.352222
Minimum0
Maximum143
Zeros4335
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:46.177717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q324
95-th percentile65
Maximum143
Range143
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.02034
Coefficient of variation (CV)1.3266508
Kurtosis6.0423134
Mean17.352222
Median Absolute Deviation (MAD)10
Skewness2.2070486
Sum215879
Variance529.93606
MonotonicityNot monotonic
2023-01-21T11:58:46.249131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4335
34.8%
8 519
 
4.2%
16 416
 
3.3%
15 313
 
2.5%
10 296
 
2.4%
9 282
 
2.3%
13 262
 
2.1%
6 259
 
2.1%
14 245
 
2.0%
11 241
 
1.9%
Other values (68) 5273
42.4%
ValueCountFrequency (%)
0 4335
34.8%
1 68
 
0.5%
2 28
 
0.2%
3 66
 
0.5%
4 206
 
1.7%
5 161
 
1.3%
6 259
 
2.1%
7 161
 
1.3%
8 519
 
4.2%
9 282
 
2.3%
ValueCountFrequency (%)
143 28
 
0.2%
125 28
 
0.2%
122 28
 
0.2%
116 28
 
0.2%
115 28
 
0.2%
113 3
 
< 0.1%
98 28
 
0.2%
96 28
 
0.2%
95 112
0.9%
94 4
 
< 0.1%

LightlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct298
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.90708
Minimum0
Maximum518
Zeros662
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:46.411694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1144
median200
Q3258
95-th percentile352
Maximum518
Range518
Interquartile range (IQR)114

Descriptive statistics

Standard deviation97.241286
Coefficient of variation (CV)0.48643242
Kurtosis0.39546991
Mean199.90708
Median Absolute Deviation (MAD)58
Skewness0.025685681
Sum2487044
Variance9455.8677
MonotonicityNot monotonic
2023-01-21T11:58:46.485375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 662
 
5.3%
214 170
 
1.4%
206 165
 
1.3%
153 161
 
1.3%
195 159
 
1.3%
141 156
 
1.3%
238 148
 
1.2%
194 136
 
1.1%
258 132
 
1.1%
139 130
 
1.0%
Other values (288) 10422
83.8%
ValueCountFrequency (%)
0 662
5.3%
1 9
 
0.1%
2 45
 
0.4%
3 58
 
0.5%
4 15
 
0.1%
9 46
 
0.4%
10 8
 
0.1%
11 3
 
< 0.1%
12 20
 
0.2%
15 3
 
< 0.1%
ValueCountFrequency (%)
518 18
0.1%
513 18
0.1%
512 18
0.1%
487 18
0.1%
480 18
0.1%
475 28
0.2%
461 18
0.1%
458 18
0.1%
439 28
0.2%
432 33
0.3%

SedentaryMinutes
Real number (ℝ)

Distinct451
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean799.19476
Minimum0
Maximum1440
Zeros26
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:46.558363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile472
Q1659
median734
Q3853
95-th percentile1440
Maximum1440
Range1440
Interquartile range (IQR)194

Descriptive statistics

Standard deviation266.77865
Coefficient of variation (CV)0.33380931
Kurtosis0.87005147
Mean799.19476
Median Absolute Deviation (MAD)100
Skewness0.7727328
Sum9942782
Variance71170.85
MonotonicityNot monotonic
2023-01-21T11:58:46.631023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440 637
 
5.1%
692 169
 
1.4%
680 149
 
1.2%
709 144
 
1.2%
745 114
 
0.9%
621 113
 
0.9%
728 107
 
0.9%
676 103
 
0.8%
732 97
 
0.8%
712 89
 
0.7%
Other values (441) 10719
86.2%
ValueCountFrequency (%)
0 26
0.2%
2 15
0.1%
13 28
0.2%
48 15
0.1%
111 2
 
< 0.1%
125 18
0.1%
127 31
0.2%
218 3
 
< 0.1%
222 31
0.2%
241 28
0.2%
ValueCountFrequency (%)
1440 637
5.1%
1439 9
 
0.1%
1438 19
 
0.2%
1437 32
 
0.3%
1431 15
 
0.1%
1430 8
 
0.1%
1428 15
 
0.1%
1423 5
 
< 0.1%
1420 3
 
< 0.1%
1413 5
 
< 0.1%

Calories
Real number (ℝ)

Distinct576
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2329.1432
Minimum0
Maximum4900
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:46.706718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1401
Q11783
median2162
Q32865
95-th percentile3787
Maximum4900
Range4900
Interquartile range (IQR)1082

Descriptive statistics

Standard deviation762.02713
Coefficient of variation (CV)0.32717059
Kurtosis0.36327672
Mean2329.1432
Median Absolute Deviation (MAD)489
Skewness0.54994432
Sum28976870
Variance580685.35
MonotonicityNot monotonic
2023-01-21T11:58:46.781561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1688 135
 
1.1%
1980 104
 
0.8%
1819 102
 
0.8%
3061 84
 
0.7%
2361 77
 
0.6%
2194 74
 
0.6%
1496 72
 
0.6%
2105 67
 
0.5%
2066 67
 
0.5%
1718 62
 
0.5%
Other values (566) 11597
93.2%
ValueCountFrequency (%)
0 25
0.2%
52 28
0.2%
57 15
0.1%
120 2
 
< 0.1%
257 26
0.2%
403 15
0.1%
665 3
 
< 0.1%
741 31
0.2%
928 31
0.2%
1032 3
 
< 0.1%
ValueCountFrequency (%)
4900 18
0.1%
4552 26
0.2%
4546 26
0.2%
4501 26
0.2%
4392 26
0.2%
4274 26
0.2%
4236 32
0.3%
4163 32
0.3%
4157 32
0.3%
4092 32
0.3%

SleepDay
Categorical

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size194.4 KiB
4/15/2016 12:00:00 AM
 
505
5/1/2016 12:00:00 AM
 
484
4/28/2016 12:00:00 AM
 
476
4/30/2016 12:00:00 AM
 
460
4/20/2016 12:00:00 AM
 
458
Other values (26)
10058 

Length

Max length21
Median length21
Mean length20.712161
Min length20

Characters and Unicode

Total characters257680
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/12/2016 12:00:00 AM
2nd row4/13/2016 12:00:00 AM
3rd row4/15/2016 12:00:00 AM
4th row4/16/2016 12:00:00 AM
5th row4/17/2016 12:00:00 AM

Common Values

ValueCountFrequency (%)
4/15/2016 12:00:00 AM 505
 
4.1%
5/1/2016 12:00:00 AM 484
 
3.9%
4/28/2016 12:00:00 AM 476
 
3.8%
4/30/2016 12:00:00 AM 460
 
3.7%
4/20/2016 12:00:00 AM 458
 
3.7%
4/21/2016 12:00:00 AM 447
 
3.6%
4/23/2016 12:00:00 AM 445
 
3.6%
4/29/2016 12:00:00 AM 444
 
3.6%
5/7/2016 12:00:00 AM 430
 
3.5%
5/8/2016 12:00:00 AM 429
 
3.4%
Other values (21) 7863
63.2%

Length

2023-01-21T11:58:46.845927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 12441
33.3%
am 12441
33.3%
4/15/2016 505
 
1.4%
5/1/2016 484
 
1.3%
4/28/2016 476
 
1.3%
4/30/2016 460
 
1.2%
4/20/2016 458
 
1.2%
4/21/2016 447
 
1.2%
4/23/2016 445
 
1.2%
4/29/2016 444
 
1.2%
Other values (23) 8722
23.4%

Most occurring characters

ValueCountFrequency (%)
0 63493
24.6%
2 30569
11.9%
1 30294
11.8%
/ 24882
 
9.7%
24882
 
9.7%
: 24882
 
9.7%
6 13652
 
5.3%
A 12441
 
4.8%
M 12441
 
4.8%
4 9036
 
3.5%
Other values (5) 11108
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158152
61.4%
Other Punctuation 49764
 
19.3%
Space Separator 24882
 
9.7%
Uppercase Letter 24882
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 63493
40.1%
2 30569
19.3%
1 30294
19.2%
6 13652
 
8.6%
4 9036
 
5.7%
5 5830
 
3.7%
3 1685
 
1.1%
9 1198
 
0.8%
7 1198
 
0.8%
8 1197
 
0.8%
Other Punctuation
ValueCountFrequency (%)
/ 24882
50.0%
: 24882
50.0%
Uppercase Letter
ValueCountFrequency (%)
A 12441
50.0%
M 12441
50.0%
Space Separator
ValueCountFrequency (%)
24882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 232798
90.3%
Latin 24882
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 63493
27.3%
2 30569
13.1%
1 30294
13.0%
/ 24882
 
10.7%
24882
 
10.7%
: 24882
 
10.7%
6 13652
 
5.9%
4 9036
 
3.9%
5 5830
 
2.5%
3 1685
 
0.7%
Other values (3) 3593
 
1.5%
Latin
ValueCountFrequency (%)
A 12441
50.0%
M 12441
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 257680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63493
24.6%
2 30569
11.9%
1 30294
11.8%
/ 24882
 
9.7%
24882
 
9.7%
: 24882
 
9.7%
6 13652
 
5.3%
A 12441
 
4.8%
M 12441
 
4.8%
4 9036
 
3.5%
Other values (5) 11108
 
4.3%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size194.4 KiB
1
11032 
2
1316 
3
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12441
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 11032
88.7%
2 1316
 
10.6%
3 93
 
0.7%

Length

2023-01-21T11:58:46.899481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-21T11:58:46.964268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11032
88.7%
2 1316
 
10.6%
3 93
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1 11032
88.7%
2 1316
 
10.6%
3 93
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12441
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11032
88.7%
2 1316
 
10.6%
3 93
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 12441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11032
88.7%
2 1316
 
10.6%
3 93
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11032
88.7%
2 1316
 
10.6%
3 93
 
0.7%

TotalMinutesAsleep
Real number (ℝ)

Distinct256
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.406
Minimum58
Maximum796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:47.023757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile166
Q1361
median432
Q3492
95-th percentile591
Maximum796
Range738
Interquartile range (IQR)131

Descriptive statistics

Standard deviation118.64372
Coefficient of variation (CV)0.28288512
Kurtosis1.5421465
Mean419.406
Median Absolute Deviation (MAD)66
Skewness-0.59890256
Sum5217830
Variance14076.332
MonotonicityNot monotonic
2023-01-21T11:58:47.095343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442 204
 
1.6%
388 155
 
1.2%
441 155
 
1.2%
412 153
 
1.2%
322 124
 
1.0%
357 124
 
1.0%
520 124
 
1.0%
523 124
 
1.0%
421 123
 
1.0%
354 123
 
1.0%
Other values (246) 11032
88.7%
ValueCountFrequency (%)
58 26
0.2%
59 31
0.2%
61 31
0.2%
62 31
0.2%
74 61
0.5%
77 31
0.2%
79 26
0.2%
82 31
0.2%
98 31
0.2%
99 31
0.2%
ValueCountFrequency (%)
796 30
0.2%
775 31
0.2%
750 31
0.2%
722 31
0.2%
700 31
0.2%
692 31
0.2%
681 31
0.2%
658 59
0.5%
651 31
0.2%
644 31
0.2%

TotalTimeInBed
Real number (ℝ)

Distinct242
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458.3605
Minimum61
Maximum961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size194.4 KiB
2023-01-21T11:58:47.172553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile179
Q1402
median463
Q3526
95-th percentile634
Maximum961
Range900
Interquartile range (IQR)124

Descriptive statistics

Standard deviation127.50607
Coefficient of variation (CV)0.27817856
Kurtosis3.4000832
Mean458.3605
Median Absolute Deviation (MAD)61
Skewness-0.19520382
Sum5702463
Variance16257.797
MonotonicityNot monotonic
2023-01-21T11:58:47.242533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
402 185
 
1.5%
546 183
 
1.5%
458 154
 
1.2%
543 154
 
1.2%
510 151
 
1.2%
501 124
 
1.0%
391 124
 
1.0%
457 124
 
1.0%
961 123
 
1.0%
500 123
 
1.0%
Other values (232) 10996
88.4%
ValueCountFrequency (%)
61 26
0.2%
65 62
0.5%
69 31
0.2%
75 31
0.2%
77 31
0.2%
78 30
0.2%
82 26
0.2%
85 31
0.2%
104 31
0.2%
107 31
0.2%
ValueCountFrequency (%)
961 123
1.0%
843 31
 
0.2%
775 31
 
0.2%
725 31
 
0.2%
722 31
 
0.2%
712 31
 
0.2%
704 31
 
0.2%
698 28
 
0.2%
689 31
 
0.2%
686 62
0.5%

Interactions

2023-01-21T11:58:43.294401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:26.839263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.003591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.023506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.176836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.333597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.360502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.502673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.543704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.656166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.699554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.791355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.845605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.962194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.114321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.131841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.359075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:26.920962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.068738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.181600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.245050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.398847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.427758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.569569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.609351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.725371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.764727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.859833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.911458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.029852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.180376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.200175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.418203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:26.990560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.129428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.244262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.309031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.459895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.489309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.630824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.669896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.788758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.823673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.921931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.061569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.092603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.240211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.262951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.483146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.065965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.195119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.313270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.378146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.526718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.557286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.698234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.735602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.856081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.888784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.989350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.127596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.161210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.305903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.331485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.550054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.206123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.261829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.382273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.447872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.593514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.626990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.766887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.802001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.924201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.953908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.058649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.194523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.230076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.371917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.400932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.614459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.271031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.324147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.446947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.513347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.655595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.691854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.830615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.864733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.987845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.102951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.122478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.257347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.295985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.434269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.466422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.681863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.341182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.389060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.515705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.581433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.721306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.759755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.897683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.930324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.054947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.167445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.189988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.322933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.363708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.499802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.534963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.745546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.408195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.453927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.582796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.648774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.785555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.825927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.963414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.086161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.119963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.230311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.256568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.387770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.429566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.563649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.602016image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.807134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.471790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.517221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.648447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.714169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.848747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.890003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.026874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.149140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.183398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.291463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.320835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.450764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.494142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.625856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.667748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.870068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.538894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.580481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.715365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.780788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.912241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.955230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.091611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.212811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.247548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.354128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.386723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.514278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.560239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.689818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.734528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.930179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.602053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.641025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.777923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.844196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.973282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.106783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.154284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.273626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.309499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.413466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.449615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.575884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.623717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.750149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.802508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.995033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.670335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.706456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.846056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.913417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.039505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.175803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.220558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.339425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.376877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.478245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.516913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.641937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.692390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.816356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.870864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:44.057966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.736722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.768863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.911135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.978859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.102131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.240506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.285045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.401832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.440227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.539859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.581642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.704590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.757795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.878692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.026881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:44.122946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.805979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.835000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:29.979324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.136438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.167911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.307204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.351769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.467724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.506588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.604791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.649713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.770861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.827343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.943961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.095089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:44.184064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.870834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.896668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.045166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.200490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.230941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.372197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.413852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.529716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.570352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.665503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.713894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.833621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:40.893195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.004932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.160221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:44.249484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:27.939945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:28.962483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:30.114129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:31.269964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:32.297832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:33.440540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:34.481887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:35.595186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:36.637454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:37.731661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:38.783339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:39.900769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:41.051287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:42.070986image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T11:58:43.230065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-21T11:58:47.314129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IdTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesTotalMinutesAsleepTotalTimeInBedActivityDateSleepDayTotalSleepRecords
Id1.000-0.0290.0280.0260.3190.1510.007-0.070-0.0190.2140.022-0.209-0.0400.4260.1180.0020.0000.1480.165
TotalSteps-0.0291.0000.9830.9840.1440.7640.7660.6350.0310.7130.7340.472-0.2700.404-0.128-0.0810.2410.0300.052
TotalDistance0.0280.9831.0001.0000.1940.7660.7620.6510.0420.7210.7290.446-0.2560.502-0.123-0.0820.2490.0370.066
TrackerDistance0.0260.9841.0001.0000.1860.7650.7610.6520.0280.7210.7290.446-0.2560.503-0.123-0.0820.2500.0370.066
LoggedActivitiesDistance0.3190.1440.1940.1861.0000.2330.1210.1290.2110.3360.070-0.0690.0020.3020.0200.0150.2040.0420.019
VeryActiveDistance0.1510.7640.7660.7650.2331.0000.7180.2110.0410.9730.7210.021-0.0990.437-0.154-0.1400.2080.0500.029
ModeratelyActiveDistance0.0070.7660.7620.7610.1210.7181.0000.3580.0430.6990.9700.180-0.1620.325-0.189-0.1070.2560.0000.020
LightActiveDistance-0.0700.6350.6510.6520.1290.2110.3581.0000.0030.2080.3060.853-0.3230.4230.0230.0020.2470.0350.052
SedentaryActiveDistance-0.0190.0310.0420.0280.2110.0410.0430.0031.0000.0250.0210.0120.0260.042-0.027-0.0390.1590.0000.011
VeryActiveMinutes0.2140.7130.7210.7210.3360.9730.6990.2080.0251.0000.720-0.005-0.0840.501-0.130-0.1260.2460.0000.028
FairlyActiveMinutes0.0220.7340.7290.7290.0700.7210.9700.3060.0210.7201.0000.143-0.1560.361-0.183-0.1070.2580.0000.034
LightlyActiveMinutes-0.2090.4720.4460.446-0.0690.0210.1800.8530.012-0.0050.1431.000-0.3910.1920.0580.0300.2370.0350.071
SedentaryMinutes-0.040-0.270-0.256-0.2560.002-0.099-0.162-0.3230.026-0.084-0.156-0.3911.000-0.017-0.144-0.1710.2870.0470.062
Calories0.4260.4040.5020.5030.3020.4370.3250.4230.0420.5010.3610.192-0.0171.0000.029-0.0930.2750.0000.068
TotalMinutesAsleep0.118-0.128-0.123-0.1230.020-0.154-0.1890.023-0.027-0.130-0.1830.058-0.1440.0291.0000.9180.0000.2540.298
TotalTimeInBed0.002-0.081-0.082-0.0820.015-0.140-0.1070.002-0.039-0.126-0.1070.030-0.171-0.0930.9181.0000.0000.2660.248
ActivityDate0.0000.2410.2490.2500.2040.2080.2560.2470.1590.2460.2580.2370.2870.2750.0000.0001.0000.0000.000
SleepDay0.1480.0300.0370.0370.0420.0500.0000.0350.0000.0000.0000.0350.0470.0000.2540.2660.0001.0000.314
TotalSleepRecords0.1650.0520.0660.0660.0190.0290.0200.0520.0110.0280.0340.0710.0620.0680.2980.2480.0000.3141.000

Missing values

2023-01-21T11:58:44.347675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-21T11:58:44.506329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesSleepDayTotalSleepRecordsTotalMinutesAsleepTotalTimeInBed
015039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/12/2016 12:00:00 AM1327346
115039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/13/2016 12:00:00 AM2384407
215039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/15/2016 12:00:00 AM1412442
315039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/16/2016 12:00:00 AM2340367
415039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/17/2016 12:00:00 AM1700712
515039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/19/2016 12:00:00 AM1304320
615039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/20/2016 12:00:00 AM1360377
715039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/21/2016 12:00:00 AM1325364
815039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/23/2016 12:00:00 AM1361384
915039603664/12/2016131628.58.50.01.880.556.060.0251332872819854/24/2016 12:00:00 AM1430449
IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesSleepDayTotalSleepRecordsTotalMinutesAsleepTotalTimeInBed
1243187920096655/10/201600.00.00.00.00.00.00.000048574/22/2016 12:00:00 AM1391407
1243287920096655/10/201600.00.00.00.00.00.00.000048574/23/2016 12:00:00 AM1339360
1243387920096655/10/201600.00.00.00.00.00.00.000048574/27/2016 12:00:00 AM1423428
1243487920096655/10/201600.00.00.00.00.00.00.000048574/28/2016 12:00:00 AM1402416
1243587920096655/10/201600.00.00.00.00.00.00.000048574/29/2016 12:00:00 AM1398406
1243687920096655/10/201600.00.00.00.00.00.00.000048574/30/2016 12:00:00 AM1343360
1243787920096655/10/201600.00.00.00.00.00.00.000048575/1/2016 12:00:00 AM1503527
1243887920096655/10/201600.00.00.00.00.00.00.000048575/2/2016 12:00:00 AM1415423
1243987920096655/10/201600.00.00.00.00.00.00.000048575/3/2016 12:00:00 AM1516545
1244087920096655/10/201600.00.00.00.00.00.00.000048575/4/2016 12:00:00 AM1439463

Duplicate rows

Most frequently occurring

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesSleepDayTotalSleepRecordsTotalMinutesAsleepTotalTimeInBed# duplicates
043881618474/12/2016101227.787.780.00.000.000.000.0000144029555/5/2016 12:00:00 AM14714952
143881618474/13/2016109938.458.450.00.060.633.880.0114150127530925/5/2016 12:00:00 AM14714952
243881618474/14/201688636.826.820.00.131.075.620.0103521994529985/5/2016 12:00:00 AM14714952
343881618474/15/201687586.736.730.00.000.006.730.00029983730665/5/2016 12:00:00 AM14714952
443881618474/16/201665805.065.060.00.210.404.450.06925360930735/5/2016 12:00:00 AM14714952
543881618474/17/201646603.583.580.00.000.003.580.00020172125725/5/2016 12:00:00 AM14714952
643881618474/18/2016110099.109.100.03.560.405.140.0278239101732745/5/2016 12:00:00 AM14714952
743881618474/19/2016101817.837.830.01.370.695.770.0201624970430155/5/2016 12:00:00 AM14714952
843881618474/20/2016105538.128.120.01.101.725.290.0194222869630835/5/2016 12:00:00 AM14714952
943881618474/21/2016100557.737.730.00.370.396.980.071227285330695/5/2016 12:00:00 AM14714952